SliceTCA
This library provides tools to perform sliceTCA.
Installation
pip install slicetca
Full documentation
The full documentation can be found here.
Examples
Quick example
import slicetca
import torch
from matplotlib import pyplot as plt
device = ('cuda' if torch.cuda.is_available() else 'cpu')
# your_data is a numpy array of shape (trials, neurons, time).
data = torch.tensor(your_data, dtype=torch.float, device=device)
# The tensor is decomposed into 2 trial-, 0 neuron- and 3 time-slicing components.
components, model = slicetca.decompose(data, (2,0,3))
# For a not positive decomposition, we apply uniqueness constraints
model = slicetca.invariance(model)
slicetca.plot(model)
plt.show()Notebook
See the example notebook for an application of sliceTCA to publicly available neural data.
Reference
A. Pellegrino@†, H. Stein†, N. A. Cayco-Gaijc@. (2023). Disentangling Mixed Classes of Covariability in Large-Scale Neural Data. https://www.biorxiv.org/content/10.1101/2023.03.01.530616v1.